Linear grids are anti-scatter devices that are used to improve contrast and the signal to noise ratio in radiographic images. A grid typically consists of a series of lead foil strips separated by spacers that are transmissive to x-rays. The spacing of the strips determines the grid frequency, and the height-to-distance between lead strips determines the grid ratio. Grids can be oriented horizontally or vertically relative to the imaging medium.
There are two general methods of use for grids: moving (Bucky-Potter configuration) and stationary. For moving type grids, the shadows of the lead strips are blurred out by motion, which can be either reciprocating or unidirectional (single stroke). For stationary grids, the shadows of the lead strips are imposed onto the radiographic image. In cases where a moving grid does not reciprocate properly or the time of exposure is faster than the time it takes for the grid to move, the resulting image can also exhibit unwanted lead strip shadows.
The pattern formed by the grids can cause image artifacts that resemble moire patterns and can hinder diagnostic interpretation of the x-ray image. One undesirable affect of grid patterns is aliasing, introduced in discrete sampling of the image by the scanning system. Factors that contribute to the aliasing are the grid resolution (grid line frequency), the sampling frequency, and the modulation transfer function (MTF) of the image acquisition device. The most typical manifestation of the problem occurs when an image is reduced in size for the purpose of soft copy presentation on a display monitor, such as on a liquid-crystal display or a cathode-ray tube (CRT), for example.
Grid use in x-ray imaging is optional and different radiology departments may have different practices related to grid use. Thus, a first step in grid artifact correction is detection of grid effects in the image content, due either to the use of a stationary grid or to malfunction of a moving Bucky-Potter grid device. One technique for grid detection is described in U.S. Pat. No. 5,661,818, issued Aug. 26, 1997, to Gaborski et. al., who describe a grid detection method that bases its detection decision on a double auto-correlation calculation. Variances are measured independently, both horizontally and vertically, and a statistical F test is performed to determine if the variances are the same over a randomly chosen sampling of locations within the image. Votes are then tallied and if a majority indicates that the variances are different, a decision is made in favor of a grid being present. This method is useful for grid detection; however, it does not provide any characteristic information about the nature of the grid that has been detected, nor does it provide information on variables such as the grid line frequency(s), the noise power of the grid, or other parameters. This type of information is important for an automated solution that compensates for grid aliasing and suppressing the grid lines.
Once a grid is detected, the grid shadows are preferably either removed or suppressed. These shadows can be considered a form of correlated noise in the image. Well known methods exist to characterize and eliminate correlated noise. However, it can be difficult to apply correction, since the frequency of grid lines within a given device can be quite variable due, in part, to the manual nature of the manufacturing process. Because of this, 2-D Fourier filtering methods and other methods that use bandstop filters can be less straightforward and prone to the introduction of artifacts if the filter is incorrectly designed. Also, in order to meet near real-time speed requirements, the commercial viability of such methods generally requires special-purpose, dedicated processing hardware due to the relatively large format of the image (2K×2K up to 4K×4K, at 12 bits/pixel). Spatial filtering is the next best choice, such as convolution with a blurring filter. But such a solution, if applied indiscriminately, often results in a global reduction of image detail. Adaptive filtering methods have been found to be appropriate for grid detection and suppression.
The problem of grid detection and suppression is further complicated where Dual-Energy (DE) imaging is used. In Dual-Energy (DE) radiography, two x-ray images of a subject are acquired by a Digital Radiography (DR) system at different energy levels, wherein the images are obtained either at the same time (using two different sets of imaging pixel sensors with one or more suitable filters on the detector) or successively, within a short time interval. The low-energy image is generally acquired first, with an exposure interval typically in the 100-300 msec range. The high-energy image is then acquired, typically within 1 second of the low-energy image, with an exposure interval in the 10-30 msec range. The two images are registered to each other, then used to decompose the imaged anatomy into separate soft-tissue and bone images. DE imaging and image processing for DE images is described, for example, in U.S. Pat. No. 6,816,572 entitled “Method, System and Computer Product for Processing Dual-Energy Images” to Jabri et al. In general, a grid is needed for DE imaging in order to reduce scatter, particularly for the low-energy image.
U.S. Pat. No. 7,627,084 entitled “Image Acquisition and Processing Chain for Dual-Energy Radiography Using a Portable Flat Panel Detector” to Jabri et al., addresses issues related to image processing and noise correction for DE imaging, including grid artifact elimination. In the sequence described by Jabri et al., various pre-processing and post-processing techniques are used for both the high-energy and low-energy images. Grid artifact elimination is applied separately to each soft tissue and bone image in a post-processing sequence that follows image decomposition.
It has been noted, however, that a number of problems result when applying grid suppression to the decomposed soft tissue and bone images. For example, aliasing can cause problems with identifying the appropriate frequency and spatial locations from which to remove the offending grid line artifacts. A further complicating factor relates to the relative contrast of the grid lines in each of the high and low energy images. Because these contrast values can be different and can have a different impact on processing each image type, the results of grid suppression can be disappointing when used in image post-processing.
Dual energy imaging is one type of imaging technique in a larger class of imaging methods that generate composite images, that is, images that are obtained by combining two or more images taken at different energy levels, at different angles, or with a change to some other variable between images.
Another imaging modality in this class that uses combined image data is limited-angle digital tomosynthesis (DTS). In tomosynthesis, the relative positions of the x-ray source and detector are changed between each of two or more images, and the images are then combined to produce 3-D views of a subject. DTS is used, for example, in angiography, chest imaging, mammography, dental imaging, and orthopaedic imaging.
Yet another type of imaging modality that uses combined image data from multiple views is cone beam computerized tomography (CBCT).
With both DTS and CBCT methods, some form of grid detection and suppression may be needed, as is needed with DE imaging.
Thus, there is a need for an image processing method that provides anti-scatter grid suppression for dual energy, digital tomosynthesis, computerized tomography, and for other types of composite imaging.